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• A decomposition-based dynamic prediction hybrid model is proposed. • A novel serial-parallel dynamic echo state network (SP-ESN) has been developed. • Phase space reconstruction and CCOA are used in SP-ESN model. • The proposed hybrid model is verified using 14 comparison models. Multiple reservoirs have been widely used in nonlinear time series forecasting. The hybrid approach is recognized as the mainstream forecasting method in complex wind speed prediction systems. However, application research and error correction of the multiple reservoirs hybrid model in the wind energy domain are still insufficient. Therefore, a serial-parallel dynamic echo state network (SP-ESN) with dual dynamic characteristics is proposed firstly. Its serial structure can capture the short-term memory information of the sequence and dynamically select the length of the training set, while the parallel structure has the ability to automatically correct the prediction error of the serial structure. Then, a dynamic prediction model based on phase space reconstruction that includes the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, the SP-ESN and the chaotic coyote optimization algorithm (CCOA) is designed in this paper. After data preprocessing of the original sequence by ICEEMDAN, the generated decomposed subsequence is input into the SP-ESN model for prediction and the final forecasting value is obtained accordingly. The CCOA is used to optimize the developed SP-ESN model parameters. Five wind speed datasets from Ningxia Hui Autonomous Region and Qinghai Province of China are selected for experiments. Six point prediction evaluation metric, forecasting efficiency and Diebold-Mariano test are used to evaluate the performance of the proposed model in ultra-short term wind speed forecasting. The case study results show that the proposed hybrid model achieves satisfactory performance compared with 5 datasets and 14 models, with mean absolute percentage error (MAPE) of 0.8405%, 1.2207%, 0.5163% 0.9791% and 0.3826%, respectively. [ABSTRACT FROM AUTHOR]